October 15, 2026
On October 7, we convened private markets leaders, clients, and partners in New York City around a practical question: where can AI improve operating leverage, financial controls, and investment decision-making today—and where does the business case remain unproven?
By the end of the afternoon, a clear answer had emerged. The firms generating tangible value from AI are not simply deploying the newest tools. They are connecting investment and accounting data, producing decision-grade outputs, and identifying portfolio and operational risk earlier. Those priorities – data integrity, control, and forward-looking intelligence – ran through nearly every conversation, from the main stage to the demo floor.
Financial intelligence starts with governed data
Ivan Latanision, our Chief Product Officer, opened by painting a portrait of intensifying competition, higher investor reporting expectations, broader access to private markets, and rapidly advancing AI models. The underlying constraint, however, is financial data architecture. When positions, transactions, valuations, cash flows, and investor data remain fragmented across systems, even sophisticated models cannot reliably support performance analysis or risk management.
Ivan and Dmitri Sedov, our Chief Data & Analytics Officer, framed the solution as an operating architecture. A governed data foundation serves as the system of record. Core applications for investment management, financial operations, and investor relations sit above it, followed by market intelligence and AI. With consistent data definitions and controls at the foundation, firms can produce more reliable analytics, reporting, and workflow automation.
That architecture was visible throughout the afternoon. Document IQ converts credit agreements, borrower financials, and loan notices into structured data, with each field linked to its source. OneLedger aligns Investment Accounting and Fund Accounting so books, records, and downstream reporting remain consistent. In the fund operations session, the discussion focused on moving from manual handoffs toward controlled, exception-based workflows, supporting faster processing, stronger data lineage, and greater operating leverage.
Controls turn AI output into decision-grade information
If governed data is the foundation, a strong control environment is what makes AI usable in financial workflows. That was central to the discussion of MCP, or Model Context Protocol: a secure connector that allows AI tools to work with Allvue data. A user can ask, “What is Fund A’s net IRR as of Q2?” from Allvue or an assistant such as Claude or ChatGPT and receive an answer governed by the same permissions and business logic as the underlying platform.
The question you type is just the tip of the iceberg. Beneath it are user-level entitlements, client data segregation, audit trails, and a commitment that client data is not returned to AI vendors, all controls that strengthen data governance, regulatory readiness, and the traceability of financial outputs. The same security, controls, and business logic stand behind every connector. These capabilities are live today within Allvue’’s Nexius platform and Investment Management.
That same principle shaped how Allvue approaches automation. The objective is to preserve accountability for material judgments while automating repeatable processing. Loan notice processing converts rate resets, paydowns, and other notices into structured data ready for validation and posting. Cash reconciliation compares expected and actual cash, directing teams to genuine exceptions rather than every transaction. This human-in-the-loop model is designed to reduce operational risk, improve control over the books, and focus finance teams on review and resolution.
Shift from lagging reports to forward-looking risk
Automation isn’t just about efficiency gains. It’s also about identifying and addressing risk faster. Several sessions pointed toward a more forward-looking operating model in which risk indicators are monitored before they become realized losses or reporting issues.
In “Beyond the Breach,” we demonstrated AI reading a credit agreement, extracting covenant terms, and monitoring headroom so private credit teams can identify deterioration before a breach occurs.
Dmitri and David Little of Moody’s discussed the new third-party risk model for private credit, which pairs Moody’s modeling with Allvue’s data to surface both hard and soft warning signs before a payment is missed. The Allvue Risk Score adds an early, standardized view of borrower risk on a 0-to-100 scale.
For CLO and public credit managers, the discussion focused on scaling portfolio management and operations. An expanded platform, AI-assisted automation, and partnerships with Versana and 9fin help managers scale across CLOs, asset-based finance, and credit secondaries without overloading the operating model. And in the carry panel, with practitioners from Lightspeed and Blue Owl, the conversation turned to why a live, current view of carry value matters more as carry-backed lending puts those valuations under a brighter spotlight.
From AI activity to enterprise value
Two fireside chats bookended the day. Jake Walker of Apollo joined Mack Santora, our Head of AI, to separate AI hype from meaningful impact in private markets. To close, David Breach of Vista Equity Partners joined our CEO, Marc Scheipe, to look at where the industry is headed as AI adoption scales.
Looking ahead, our roadmap centers on four areas: AI-native automation, unified and open data, intelligence and insights, and expanded coverage. Near-term milestones include query connectors for Financial Operations and Investor Relations arriving in November, Moody’s EDF-X Private Credit and ICE Indicative Pricing by year end, and Portfolio Intelligence for private equity in Q1 2027.
Translate our roadmap into your operating plan
Engage your CSM or account executive to identify the highest-value starting point—agentic workflows, MCP, analytics, or a combination. We will assess data and control readiness, prioritize workflows, and define a phased plan tied to outcomes such as reporting quality, exception reduction, risk visibility, and operating leverage.
Thank you to everyone who joined us in New York. The takeaway was clear: AI creates durable financial value when it is built on governed data, embedded in controlled workflows, and applied to decisions that affect performance, liquidity, valuation, and risk.